Bayesian approaches to the model selection problem in the analysis of latent stage-sequential process
DOI10.1016/j.csda.2012.03.015zbMath1255.62307OpenAlexW1994863710MaRDI QIDQ1927188
Publication date: 30 December 2012
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2012.03.015
longitudinal dataDirichlet processfinite mixture modelreversible jump MCMClatent class analysisstage-sequential process
Bayesian inference (62F15) Sequential statistical methods (62L99) Inference from stochastic processes (62M99) Numerical analysis or methods applied to Markov chains (65C40) Nonparametric inference (62G99)
Related Items (2)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Flexible modelling of random effects in linear mixed models -- a Bayesian approach
- Ferguson distributions via Polya urn schemes
- Nonparametric hierarchical Bayes via sequential imputations
- A Bayesian analysis of some nonparametric problems
- Contributions to the theory of Dirichlet processes
- Nonparametric Bayes Conditional Distribution Modeling With Variable Selection
- Latent Structure Analysis of a Set of Multidimensional Contingency Tables
- Sampling-Based Approaches to Calculating Marginal Densities
- Kernel stick-breaking processes
- The Calculation of Posterior Distributions by Data Augmentation
- Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
- Exploratory latent structure analysis using both identifiable and unidentifiable models
- Bayesian Density Estimation and Inference Using Mixtures
This page was built for publication: Bayesian approaches to the model selection problem in the analysis of latent stage-sequential process
